Data Analysis on Aspiring Minds Employability OutcomesData

By- Suraj Honkamble

The Dataset description

The dataset was released by Aspiring Minds from the Aspiring Mind Employment Outcome 2015 (AMEO). The study is primarily limited only to students with engineering disciplines. The dataset contains the employment outcomes of engineering graduates as dependent variables (Salary, Job Titles, and Job Locations) along with the standardized scores from three different areas – cognitive skills, technical skills and personality skills. The dataset also contains demographic features. The dataset contains around 40 independent variables and 4000 data points. The independent variables are both continuous and categorical in nature. The dataset contains a unique identifier for each candidate.

Research Questions

1. Times of India article dated Jan 18, 2019 states that “After doing your Computer Science Engineering if you take up jobs as a Programming Analyst, Software Engineer, Hardware Engineer and Associate Engineer you can earn up to 2.5-3 lakhs as a fresh graduate.” Test this claim with the data given to you.
2. Is there a relationship between gender and specialisation? (i.e. Does the preference of Specialisation depend on the Gender?)

Import required libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
import plotly.express as px
import plotly.graph_objects as go
import warnings
warnings.filterwarnings('ignore')

Load and read the dataset

pd.options.display.max_columns=50

df=pd.read_excel('D:\DATA SCIENCE Internship with Innomatics\Data Analysis\\aspiring_minds_employability_outcomes_2015.xlsx')
df.head()

	Unnamed: 0	ID	Salary	DOJ	DOL	Designation	JobCity	Gender	DOB	10percentage	10board	12graduation	12percentage	12board	CollegeID	CollegeTier	Degree	Specialization	collegeGPA	CollegeCityID	CollegeCityTier	CollegeState	GraduationYear	English	Logical	Quant	Domain	ComputerProgramming	ElectronicsAndSemicon	ComputerScience	MechanicalEngg	ElectricalEngg	TelecomEngg	CivilEngg	conscientiousness	agreeableness	extraversion	nueroticism	openess_to_experience
0	train	203097	420000	2012-06-01	present	senior quality engineer	Bangalore	f	1990-02-19	84.3	board ofsecondary education,ap	2007	95.8	board of intermediate education,ap	1141	2	B.Tech/B.E.	computer engineering	78.00	1141	0	Andhra Pradesh	2011	515	585	525	0.635979	445	-1	-1	-1	-1	-1	-1	0.9737	0.8128	0.5269	1.35490	-0.4455
1	train	579905	500000	2013-09-01	present	assistant manager	Indore	m	1989-10-04	85.4	cbse	2007	85.0	cbse	5807	2	B.Tech/B.E.	electronics and communication engineering	70.06	5807	0	Madhya Pradesh	2012	695	610	780	0.960603	-1	466	-1	-1	-1	-1	-1	-0.7335	0.3789	1.2396	-0.10760	0.8637
2	train	810601	325000	2014-06-01	present	systems engineer	Chennai	f	1992-08-03	85.0	cbse	2010	68.2	cbse	64	2	B.Tech/B.E.	information technology	70.00	64	0	Uttar Pradesh	2014	615	545	370	0.450877	395	-1	-1	-1	-1	-1	-1	0.2718	1.7109	0.1637	-0.86820	0.6721
3	train	267447	1100000	2011-07-01	present	senior software engineer	Gurgaon	m	1989-12-05	85.6	cbse	2007	83.6	cbse	6920	1	B.Tech/B.E.	computer engineering	74.64	6920	1	Delhi	2011	635	585	625	0.974396	615	-1	-1	-1	-1	-1	-1	0.0464	0.3448	-0.3440	-0.40780	-0.9194
4	train	343523	200000	2014-03-01	2015-03-01 00:00:00	get	Manesar	m	1991-02-27	78.0	cbse	2008	76.8	cbse	11368	2	B.Tech/B.E.	electronics and communication engineering	73.90	11368	0	Uttar Pradesh	2012	545	625	465	0.124502	-1	233	-1	-1	-1	-1	-1	-0.8810	-0.2793	-1.0697	0.09163	-0.1295

Column Information

• ID - A unique ID to identify a candidate
• Salary - Annual CTC offered to the candidate (in INR)
• DOJ - Date of joining the company
• DOL- Date of leaving the company
• Designation - Designation offered in the job
• JobCity - Location of the job (city)
• Gender - Candidate’s gender
• DOB - Date of birth of candidate
• 10percentage - Overall marks obtained in grade 10 examinations
• 10board - The school board whose curriculum the candidate followed in grade 10
• 12graduation - Year of graduation - senior year high school
• 12percentage - Overall marks obtained in grade 12 examinations
• 12board - The school board whose curriculum the candidate followed in grade 12
• CollegeID - Unique ID identifying the college which the candidate attended
• CollegeTier - Tier of college
• Degree - Degree obtained/pursued by the candidate
• Specialization - Specialization pursued by the candidate
• CollegeGPA - Aggregate GPA at graduation
• CollegeCityID - A unique ID to identify the city in which the college is located in
• CollegeCityTier - The tier of the city in which the college is located
• CollegeState - Name of States
• GraduationYear - Year of graduation (Bachelor’s degree)
• English - Scores in AMCAT English section
• Logical - Scores in AMCAT Logical section
• Quant - Scores in AMCAT Quantitative section
• Domain - Scores in AMCAT’s domain module
• ComputerProgramming - Score in AMCAT’s Computer programming section
• ElectronicsAndSemicon - Score in AMCAT’s Electronics & Semiconductor Engineering section
• ComputerScience - Score in AMCAT’s Computer Science section
• MechanicalEngg - Score in AMCAT’s Mechanical Engineering section
• ElectricalEngg - Score in AMCAT’s Electrical Engineering section
• TelecomEngg - Score in AMCAT’s Telecommunication Engineering section
• CivilEngg - Score in AMCAT’s Civil Engineering section
• conscientiousness - Scores in one of the sections of AMCAT’s personality test
• agreeableness - Scores in one of the sections of AMCAT’s personality test (Standardized)
• extraversion - Scores in one of the sections of AMCAT’s personality test (Standardized)
• neuroticism - Scores in one of the sections of AMCAT’s personality test (Standardized)
• openess_to_experience - Scores in one of the sections of AMCAT’s personality test (Standardized)

df.shape

(3998, 39)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3998 entries, 0 to 3997
Data columns (total 39 columns):
 #   Column                 Non-Null Count  Dtype         
---  ------                 --------------  -----         
 0   Unnamed: 0             3998 non-null   object        
 1   ID                     3998 non-null   int64         
 2   Salary                 3998 non-null   int64         
 3   DOJ                    3998 non-null   datetime64[ns]
 4   DOL                    3998 non-null   object        
 5   Designation            3998 non-null   object        
 6   JobCity                3998 non-null   object        
 7   Gender                 3998 non-null   object        
 8   DOB                    3998 non-null   datetime64[ns]
 9   10percentage           3998 non-null   float64       
 10  10board                3998 non-null   object        
 11  12graduation           3998 non-null   int64         
 12  12percentage           3998 non-null   float64       
 13  12board                3998 non-null   object        
 14  CollegeID              3998 non-null   int64         
 15  CollegeTier            3998 non-null   int64         
 16  Degree                 3998 non-null   object        
 17  Specialization         3998 non-null   object        
 18  collegeGPA             3998 non-null   float64       
 19  CollegeCityID          3998 non-null   int64         
 20  CollegeCityTier        3998 non-null   int64         
 21  CollegeState           3998 non-null   object        
 22  GraduationYear         3998 non-null   int64         
 23  English                3998 non-null   int64         
 24  Logical                3998 non-null   int64         
 25  Quant                  3998 non-null   int64         
 26  Domain                 3998 non-null   float64       
 27  ComputerProgramming    3998 non-null   int64         
 28  ElectronicsAndSemicon  3998 non-null   int64         
 29  ComputerScience        3998 non-null   int64         
 30  MechanicalEngg         3998 non-null   int64         
 31  ElectricalEngg         3998 non-null   int64         
 32  TelecomEngg            3998 non-null   int64         
 33  CivilEngg              3998 non-null   int64         
 34  conscientiousness      3998 non-null   float64       
 35  agreeableness          3998 non-null   float64       
 36  extraversion           3998 non-null   float64       
 37  nueroticism            3998 non-null   float64       
 38  openess_to_experience  3998 non-null   float64       
dtypes: datetime64[ns](2), float64(9), int64(18), object(10)
memory usage: 1.2+ MB

Missing values

df.isna().sum()

Unnamed: 0               0
ID                       0
Salary                   0
DOJ                      0
DOL                      0
Designation              0
JobCity                  0
Gender                   0
DOB                      0
10percentage             0
10board                  0
12graduation             0
12percentage             0
12board                  0
CollegeID                0
CollegeTier              0
Degree                   0
Specialization           0
collegeGPA               0
CollegeCityID            0
CollegeCityTier          0
CollegeState             0
GraduationYear           0
English                  0
Logical                  0
Quant                    0
Domain                   0
ComputerProgramming      0
ElectronicsAndSemicon    0
ComputerScience          0
MechanicalEngg           0
ElectricalEngg           0
TelecomEngg              0
CivilEngg                0
conscientiousness        0
agreeableness            0
extraversion             0
nueroticism              0
openess_to_experience    0
dtype: int64

No Missing values.

Univariate Analysis

Drop the Unnamed :0 column

df['Unnamed: 0'].unique()

array(['train'], dtype=object)

df.drop('Unnamed: 0', axis=1, inplace=True)

Analyze ID column

df['ID'].nunique()

3998

ID's are unique for each row or we can say for each employee.

Analyze Salary column

df['Salary'].dtype

dtype('int64')

Minimum, Maximum, Average and Median Salary

print("Minimum Salary", df['Salary'].min())
print("Maximum Salary", df['Salary'].max())
print("Average Salary", df['Salary'].mean())
print("Median Salary", df['Salary'].median())

Minimum Salary 35000
Maximum Salary 4000000
Average Salary 307699.8499249625
Median Salary 300000.0

Mean and Median for Salary column is nearly equal, so might be less posibility that this columns contains any outlier.

Check the distribution of data to detect outliers

plt.figure(figsize=(12,4))
sns.boxplot(df['Salary'], color='pink')
plt.title('Distribution of Salary befor Log Tranformation', fontsize=18, color='blue');

plt.figure(figsize=(12,4))
sns.distplot(df['Salary'], color='orange')
plt.title('Distribution of Salary befor Log Tranformation', fontsize=18, color='blue');

There are many outliers present in this column and data is right skewed, Lets take log transormation and check whether the distribution will change to normal or not.

plt.figure(figsize=(12,4))
sns.distplot(np.log(df['Salary']), color='orange')
plt.title('Distribution of Salary after Log Tranformation', fontsize=18, color='blue');

plt.figure(figsize=(12,4))
sns.boxplot(np.log(df['Salary']), color='pink')
plt.title('Distribution of Salary after Log Tranformation', fontsize=18, color='blue');

Now the Distribution somewhat looks similar to normal, and we are get rid of outliers which are laying far away from the group of data.

Analyze Date of Joining column

print(df['DOJ'].dtype)

datetime64[ns]

When the first and last requiretemt happen?

print("First Recruitment Date:", df['DOJ'].min())
print("Last Recruitment Date:", df['DOJ'].max())

First Recruitment Date: 1991-06-01 00:00:00
Last Recruitment Date: 2015-12-01 00:00:00

First Employee joined the Company on 1st June 1991 and last employee on 1st December 2015.

Recruitment by Year

df['Year_of_Joining']=df['DOJ'].dt.year

plt.figure(figsize=(12,4))
sns.countplot(x='Year_of_Joining', data=df);
plt.title("Recruitment by Year", fontsize=18, color='blue');

1. First recruitment is in 1991 and the last is in 2015
2. More EMployees recruited in 2014.
3. No single employee is recruited between 1991 to 2004.

Analyze Date of Leaving column

df['DOL'].dtype

dtype('O')

df['DOL'].head()

0                present
1                present
2                present
3                present
4    2015-03-01 00:00:00
Name: DOL, dtype: object

Date of Leaving should be datetime column. If the employeed still working then data point "Present". This is 2015 Employability data so the final date here should be 01-12-2015. Its better to replace "Present" with "01-01-2016". Then convert it to datetime column.

present_date='2016-01-01'

df['DOL'].replace({"present": present_date}, inplace=True)
df['DOL'].head()

0             2016-01-01
1             2016-01-01
2             2016-01-01
3             2016-01-01
4    2015-03-01 00:00:00
Name: DOL, dtype: object

df['DOL']=pd.to_datetime(df['DOL'])
print(df['DOL'].dtype)

datetime64[ns]

How many employees still working in this company?

df[df['DOL']=='2016-01-01'].shape[0]

1875

1875 Employees still working in this company.

In each year How many employees left?

df['Year_of_Leaving']=df['DOL'].dt.year
year_of_leaving=df[df['Year_of_Leaving']!=2016]
plt.figure(figsize=(12,4))
sns.countplot(x='Year_of_Leaving', data=year_of_leaving);
plt.title("Count of Employees left by each year", fontsize=18, color='blue');

Every calender year the count of employees who left the company is increasing.

Analyze Designation column

df['Designation'].dtype

dtype('O')

df['Designation'].nunique()

419

df['Designation'].unique()

array(['senior quality engineer', 'assistant manager', 'systems engineer',
       'senior software engineer', 'get', 'system engineer',
       'java software engineer', 'mechanical engineer',
       'electrical engineer', 'project engineer', 'senior php developer',
       'senior systems engineer', 'quality assurance engineer',
       'qa analyst', 'network engineer', 'product development engineer',
       'associate software developer', 'data entry operator',
       'software engineer', 'developer', 'electrical project engineer',
       'programmer analyst', 'systems analyst', 'ase',
       'telecommunication engineer', 'application developer',
       'ios developer', 'executive assistant', 'online marketing manager',
       'documentation specialist', 'associate software engineer',
       'management trainee', 'site manager', 'software developer',
       '.net developer', 'production engineer', 'jr. software engineer',
       'trainee software developer', 'ui developer',
       'assistant system engineer', 'android developer',
       'customer service', 'test engineer', 'java developer', 'engineer',
       'recruitment coordinator', 'technical support engineer',
       'data analyst', 'assistant software engineer', 'faculty',
       'entry level management trainee',
       'customer service representative', 'software test engineer',
       'firmware engineer', 'php developer', 'research associate',
       'research analyst', 'quality engineer', 'programmer',
       'technical support executive', 'business analyst', 'web developer',
       'application engineer', 'project coordinator', 'engineer trainee',
       'sap consultant', 'quality analyst', 'marketing coordinator',
       'system administrator', 'senior engineer',
       'business development managerde', 'network administrator',
       'technical support specialist', 'business development executive',
       'junior software engineer', 'asp.net developer',
       'graduate engineer trainee', 'field engineer',
       'assistant professor', 'trainee software engineer',
       'senior software developer',
       'quality assurance automation engineer', 'design engineer',
       'telecom engineer', 'quality control engineer',
       'hardware engineer', 'hr recruiter', 'sales associate',
       'junior engineer', 'associate engineer', 'maintenance engineer',
       'sales engineer', 'human resources associate',
       'mobile application developer',
       'electronic field service engineer', 'process associate',
       'field service engineer', 'it support specialist',
       'software development engineer', 'business process analyst',
       'operation engineer', 'electrical designer', 'marketing assistant',
       'sales executive', 'admin assistant', 'senior java developer',
       'account executive', 'oracle dba', 'rf engineer',
       'embedded software engineer', 'programmer analyst trainee',
       'technical engineer', 'operations executive', 'trainee engineer',
       'recruiter', 'lecturer', '.net web developer',
       'marketing executive', 'operations assistant', 'associate manager',
       'electrical design engineer', 'systems administrator',
       'client services associate', 'it analyst', 'senior developer',
       'cad designer', 'business technology analyst', 'asst. manager',
       'service engineer', 'executive recruiter', 'planning engineer',
       'associate technical operations', 'web designer',
       'software architect', 'software quality assurance tester',
       'seo trainee', 'process engineer',
       'software quality assurance analyst', 'designer',
       'business systems consultant', 'business development manager',
       'junior research fellow', 'technical recruiter',
       'operations analyst', 'quality assurance test engineer',
       'linux systems administrator', 'software trainee',
       'entry level sales and marketing', 'electrical field engineer',
       'windows systems administrator', 'junior software developer',
       'python developer', 'web application developer',
       'assistant systems engineer', 'javascript developer',
       'operation executive', 'performance engineer', 'technical writer',
       'operations engineer and jetty handling', 'lead engineer',
       'portfolio analyst', 'associate system engineer',
       'mechanical design engineer', 'product engineer',
       'network security engineer', 'operations manager',
       'technical lead', 'operations', 'quality assurance tester',
       'automation engineer', 'data scientist', 'quality associate',
       'manual tester', 'sr. engineer', 'embedded engineer',
       'service and sales engineer', 'telecom support engineer',
       'engineer- customer support', 'cloud engineer', 'branch manager',
       'business analyst consultant', 'technology lead',
       'software trainee engineer', 'dcs engineer', 'junior manager',
       'ux designer', 'clerical', 'hr generalist',
       'database administrator', 'senior design engineer', 'seo',
       'assistant engineer', 'marketing analyst', 'it executive',
       'salesforce developer', 'software tester', 'sql dba',
       'junior engineer product support', 'manager',
       'senior business analyst', 'c# developer',
       'implementation engineer', 'executive hr', 'executive engineer',
       'sharepoint developer', 'system analyst',
       'sales management trainee', 'senior project engineer',
       'it recruiter', 'software engineer analyst',
       'desktop support technician', 'continuous improvement engineer',
       'process advisor', 'etl developer', 'sales and service engineer',
       'project manager', 'training specialist', 'product manager',
       'staffing recruiter', 'assistant programmer', 'quality controller',
       'mis executive', 'game developer', 'digital marketing specialist',
       'principal software engineer', 'software devloper',
       'senior mechanical engineer', 'technical operations analyst',
       'service coordinator', 'testing engineer', 'technical assistant',
       'sap abap consultant', 'seo engineer', 'project assistant',
       'talent acquisition specialist', 'sales account manager',
       'software engineer trainee', 'customer service manager',
       'help desk analyst', 'general manager', 'engineering manager',
       'senior network engineer',
       'field based employee relations manager', 'phone banking officer',
       'support engineer', 'associate test engineer',
       'technology analyst', 'network support engineer',
       'it business analyst', 'junior system analyst',
       'senior .net developer', 'secretary', 'research engineer',
       'quality assurance auditor', 'process executive',
       'lecturer & electrical maintenance', 'office coordinator',
       'hr manager', 'html developer', 'sales support',
       'front end web developer', 'administrative support',
       'territory sales manager', 'project administrator',
       'environmental engineer', 'web designer and seo',
       'information security analyst',
       'field business development associate', 'operational executive',
       'administrative coordinator', 'senior risk consultant',
       'desktop support engineer', 'cad drafter', 'noc engineer',
       'industrial engineer', 'it engineer', 'human resources intern',
       'senior quality assurance engineer', 'clerical assistant',
       'software enginner', 'quality assurance',
       'delivery software engineer', 'graphic designer',
       'sales development manager', 'visiting faculty',
       'business intelligence analyst', 'team lead',
       'operational excellence manager', 'sales & service engineer',
       'web intern', 'full stack developer', 'database developer',
       'sr. database engineer', 'graduate apprentice trainee',
       'software engineer associate', 'technical analyst',
       'executive engg', 'it technician', 'business system analyst',
       'process control engineer', 'technical consultant',
       'business office manager', 'quality control inspector',
       'product design engineer', 'manufacturing engineer',
       'seo executive', 'sap analyst', 'software engineere',
       'financial service consultant', 'co faculty', 'software analyst',
       'desktop support analyst', 'graduate engineer',
       'engineering technician', 'it assistant', 'marketing manager',
       'human resource assistant', 'hr assistant', 'product developer',
       'customer support engineer',
       'quality control inspection technician', 'gis/cad engineer',
       'senior web developer', 'sql developer', 'research staff member',
       'sap abap associate consultant', 'associate qa',
       'corporate recruiter', 'project management officer',
       'business systems analyst', 'software programmer',
       'help desk technician', 'sales manager', 'catalog associate',
       'assistant store manager', 'software engg', 'it developer',
       'apprentice', 'business consultant', 'controls engineer',
       'ruby on rails developer', 'risk consultant', 'account manager',
       'professor', 'assistant administrator', 'civil engineer',
       'educator', 'service manager', 'teradata dba',
       'full-time loss prevention associate', 'junior recruiter',
       'associate developer', 'assistant electrical engineer',
       'shift engineer', 'dotnet developer', 'rf/dt engineer',
       'human resources analyst', 'software test engineerte',
       'junior .net developer', 'java trainee', 'maintenance supervisor',
       'r&d engineer', 'front end developer', 'engineer-hws',
       'operations engineer', 'senior research fellow',
       'web designer and joomla administrator',
       'enterprise solutions developer',
       'information technology specialist', 'site engineer',
       'graduate trainee engineer', 'quality assurance analyst',
       'cnc programmer', 'financial analyst', 'system engineer trainee',
       'sap mm consultant', 'assistant system engineer trainee',
       'qa trainee', 'teradata developer', 'hr executive',
       'senior programmer', 'software test engineer (etl)',
       'associate software engg', 'supply chain analyst', 'sales trainer',
       'software executive', 'team leader',
       'assistant system engineer - trainee', 'seo analyst',
       'risk investigator', 'executive administrative assistant',
       'program manager', 'r & d', 'sap functional consultant',
       'website developer/tester', 'software designer',
       'sales coordinator', 'qa engineer', 'aircraft technician',
       'customer care executive', 'senior test engineer',
       'program analyst trainee', 'electrical controls engineer',
       'trainee decision scientist', 'editor', 'bss engineer', 'dba',
       'software eng', 'computer faculty', 'recruitment associate',
       'logistics executive', 'quality consultant',
       'senior sales executive', 'db2 dba', 'test technician',
       'it operations associate', 'software engineering associate',
       'research scientist', 'jr. software developer'], dtype=object)

Many Designations are repeated with either in short form or with liitle spelling difference.

df['Designation'].replace({"ase":"associate software engineer", "get":"graduate engineer trainee",
                           "graduate trainee engineer":"graduate engineer trainee", "software engineer associate":"associate software engineer",
                           "business development managerde":"business development manager",
                           "jr. software developer":"junior software developer", "software trainee engineer":"software engineer trainee",
                           "asst. manager":"assistant manager", "dotnet developer":".net developer",
                           "qa analyst":"quality assurance analyst","associate software engg":"associate software engineer",
                           "assistant system engineer - trainee":"assistant system engineer trainee",
                           "software eng":"software engineer", "software engineering associate":"software engineer associate",
                           "software enginner":"software engineer", "systems engineer":"system engineer"}, inplace=True)

df['Designation'].nunique()

404

Now Capitalize the Designations so it look Proffesional Title

df['Designation']=df['Designation'].str.title()
df['Designation']

0           Senior Quality Engineer
1                 Assistant Manager
2                   System Engineer
3          Senior Software Engineer
4         Graduate Engineer Trainee
                   ...             
3993              Software Engineer
3994               Technical Writer
3995    Associate Software Engineer
3996             Software Developer
3997        Senior Systems Engineer
Name: Designation, Length: 3998, dtype: object

In which Job role most employees are working

job_role=df['Designation'].value_counts()[:20].reset_index()
plt.figure(figsize=(12,4))
sns.barplot(x='index', y='Designation', data=job_role)
plt.xticks(rotation=60, fontsize=12)
plt.ylabel("Count of Designation")
plt.xlabel("Designation")
plt.title("Count of Enployees working in specific Designation", fontsize=18, color='blue');

1. There are 405 unique designations.
2. Out of 3998 employees, 500+ employees working as Software Engineer.
3. Most of the employees are only Related to Software Development field.

Missing values JOb City Column

How many employees working as Business Analyst, Data Analyst, Python Developer and Data Scientist.

print("Total Business analysts: ", df[df['Designation']=='Business Analyst'].shape[0])
print("Total Data analysts: ", df[df['Designation']=='Data Analyst'].shape[0])
print("Total Python Developers: ", df[df['Designation']=='Python Developer'].shape[0])
print("Total Data Scientists: ", df[df['Designation']=='Data Scientist'].shape[0])

Total Business analysts:  49
Total Data analysts:  49
Total Python Developers:  1
Total Data Scientists:  3

Analyze Job cities

df['JobCity'].dtype

dtype('O')

df['JobCity'].nunique()

339

df['JobCity'].unique()

array(['Bangalore', 'Indore', 'Chennai', 'Gurgaon', 'Manesar',
       'Hyderabad', 'Banglore', 'Noida', 'Kolkata', 'Pune', -1, 'mohali',
       'Jhansi', 'Delhi', 'Hyderabad ', 'Bangalore ', 'noida', 'delhi',
       'Bhubaneswar', 'Navi Mumbai', 'Mumbai', 'New Delhi', 'Mangalore',
       'Rewari', 'Gaziabaad', 'Bhiwadi', 'Mysore', 'Rajkot',
       'Greater Noida', 'Jaipur', 'noida ', 'HYDERABAD', 'mysore',
       'THANE', 'Maharajganj', 'Thiruvananthapuram', 'Punchkula',
       'Bhubaneshwar', 'Pune ', 'coimbatore', 'Dhanbad', 'Lucknow',
       'Trivandrum', 'kolkata', 'mumbai', 'Gandhi Nagar', 'Una',
       'Daman and Diu', 'chennai', 'GURGOAN', 'vsakhapttnam', 'pune',
       'Nagpur', 'Bhagalpur', 'new delhi - jaisalmer', 'Coimbatore',
       'Ahmedabad', 'Kochi/Cochin', 'Bankura', 'Bengaluru', 'Mysore ',
       'Kanpur ', 'jaipur', 'Gurgaon ', 'bangalore', 'CHENNAI',
       'Vijayawada', 'Kochi', 'Beawar', 'Alwar', 'NOIDA', 'Greater noida',
       'Siliguri ', 'raipur', 'gurgaon', 'Bhopal', 'Faridabad', 'Jodhpur',
       'udaipur', 'Muzaffarpur', 'Kolkata`', 'Bulandshahar', 'Haridwar',
       'Raigarh', 'Visakhapatnam', 'Jabalpur', 'hyderabad', 'Unnao',
       'KOLKATA', 'Thane', 'Aurangabad', 'Belgaum', 'gurgoan', 'Dehradun',
       'Rudrapur', 'Jamshedpur', 'vizag', 'Nouda', 'Dharamshala',
       'Banagalore', 'Hissar', 'Ranchi', 'BANGALORE', 'Madurai', 'Gurga',
       'Chandigarh', 'Australia', ' Chennai', 'CHEYYAR', 'Mumbai ',
       'sonepat', 'Ghaziabad', 'Pantnagar', 'Siliguri', 'mumbai ',
       'Jagdalpur', 'Chennai ', 'angul', 'Baroda', ' ariyalur', 'Jowai',
       'Kochi/Cochin, Chennai and Coimbatore', 'bhubaneswar', 'Neemrana',
       'VIZAG', 'Tirupathi', 'Lucknow ', 'Ahmedabad ', 'Bhubneshwar',
       'Noida ', 'pune ', 'Calicut', 'Gandhinagar', 'LUCKNOW', 'Dubai',
       'bengaluru', 'MUMBAI', 'Ahmednagar', 'Nashik', 'New delhi',
       'Bellary', 'Ludhiana', 'New Delhi ', 'Muzaffarnagar', 'BHOPAL',
       'Gurgoan', 'Gagret', 'Indirapuram, Ghaziabad', 'Gwalior',
       'new delhi', 'TRIVANDRUM', 'Chennai & Mumbai', 'Rajasthan',
       'Sonipat', 'Bareli', 'Kanpur', 'Hospete', 'Miryalaguda', ' mumbai',
       'Dharuhera', 'lucknow', 'meerut', 'dehradun', 'Ganjam', 'Hubli',
       'bangalore ', 'NAVI MUMBAI', 'ncr', 'Agra', 'Trichy',
       'kudankulam ,tarapur', 'Ongole', 'Sambalpur', 'Pondicherry',
       'Bundi', 'SADULPUR,RAJGARH,DISTT-CHURU,RAJASTHAN', 'AM', 'Bikaner',
       'Vadodara', 'BAngalore', 'india', 'Asansol', 'Tirunelvelli',
       'Ernakulam', 'DELHI', 'Bilaspur', 'Chandrapur', 'Nanded',
       'Dharmapuri', 'Vandavasi', 'Rohtak', 'trivandrum', 'Nagpur ',
       'Udaipur', 'Patna', 'banglore', 'indore', 'Salem', 'Nasikcity',
       'Gandhinagar ', 'Technopark, Trivandrum', 'Bharuch', 'Tornagallu',
       'Raipur', 'Kolkata ', 'Jaspur', 'Burdwan', 'Bhubaneswar ',
       'Shimla', 'ahmedabad', 'Gajiabaad', 'Jammu', 'Shahdol',
       'Muvattupuzha', 'Al Jubail,Saudi Arabia', 'Kalmar, Sweden',
       'Secunderabad', 'A-64,sec-64,noida', 'Ratnagiri', 'Jhajjar',
       'Gulbarga', 'hyderabad(bhadurpally)', 'Nalagarh', 'Chandigarh ',
       'Jaipur ', 'Jeddah Saudi Arabia', ' Delhi', 'PATNA', 'SHAHDOL',
       'Chennai, Bangalore', 'Bhopal ', 'Jamnagar', 'PUNE', 'Tirupati',
       'Gonda', 'jamnagar', 'chennai ', 'orissa', 'kharagpur',
       'Trivandrum ', 'Navi Mumbai , Hyderabad', 'Joshimath',
       'chandigarh', 'Bathinda', 'Johannesburg', 'kala amb ', 'Karnal',
       'LONDON', 'Kota', 'Panchkula', 'Baddi HP', 'Nagari',
       'Mettur, Tamil Nadu ', 'Durgapur', 'pondi', 'Surat', 'Kurnool',
       'kolhapur', 'Madurai ', 'GREATER NOIDA', 'Bhilai', ' Pune',
       'hderabad', 'KOTA', 'thane', 'Vizag', 'Bahadurgarh',
       'Rayagada, Odisha', 'kakinada', 'GURGAON', 'Varanasi', 'punr',
       'Nellore', 'patna', 'Meerut', 'hyderabad ', 'Sahibabad', 'Howrah',
       'BHUBANESWAR', 'Trichur', 'Ambala', 'Khopoli', 'keral', 'Roorkee',
       'Greater NOIDA', 'Navi mumbai', 'ghaziabad', 'Allahabad',
       'Delhi/NCR', 'Panchkula ', 'Ranchi ', 'Jalandhar', 'manesar',
       'vapi', 'PILANI', 'muzzafarpur', 'RAS AL KHAIMAH', 'bihar',
       'singaruli', 'KANPUR', 'Banglore ', 'pondy', 'Mohali', 'Phagwara',
       ' Mumbai', ' bangalore', 'GURAGAON', 'Baripada', 'MEERUT',
       'Yamuna Nagar', 'shahibabad', 'sampla', 'Guwahati', 'Rourkela',
       'Banaglore', 'Vellore', 'Dausa', 'latur (Maharashtra )',
       'NEW DELHI', 'kanpur', 'Mainpuri', 'karnal', 'Dammam', 'Haldia',
       'sambalpur', 'RAE BARELI', 'ranchi', 'jAipur', 'BANGLORE',
       'Patiala', 'Gorakhpur', 'new dehli', 'BANGALORE ', 'Ambala City',
       'Karad', 'Rajpura', 'Pilani', 'haryana', 'Asifabadbanglore'],
      dtype=object)

Many Job Cities are repeated with some spelling change, some white spaces at beginning or at end, Some are in upper case and some are lower case, some are with old city names(the city name changed now.). Lets apply string methods to clean the names.

df['JobCity']=df['JobCity'].str.replace(" ","").str.lower()
df['JobCity'].nunique()

230

df['JobCity'].unique()

array(['bangalore', 'indore', 'chennai', 'gurgaon', 'manesar',
       'hyderabad', 'banglore', 'noida', 'kolkata', 'pune', nan, 'mohali',
       'jhansi', 'delhi', 'bhubaneswar', 'navimumbai', 'mumbai',
       'newdelhi', 'mangalore', 'rewari', 'gaziabaad', 'bhiwadi',
       'mysore', 'rajkot', 'greaternoida', 'jaipur', 'thane',
       'maharajganj', 'thiruvananthapuram', 'punchkula', 'bhubaneshwar',
       'coimbatore', 'dhanbad', 'lucknow', 'trivandrum', 'gandhinagar',
       'una', 'damananddiu', 'gurgoan', 'vsakhapttnam', 'nagpur',
       'bhagalpur', 'newdelhi-jaisalmer', 'ahmedabad', 'kochi/cochin',
       'bankura', 'bengaluru', 'kanpur', 'vijayawada', 'kochi', 'beawar',
       'alwar', 'siliguri', 'raipur', 'bhopal', 'faridabad', 'jodhpur',
       'udaipur', 'muzaffarpur', 'kolkata`', 'bulandshahar', 'haridwar',
       'raigarh', 'visakhapatnam', 'jabalpur', 'unnao', 'aurangabad',
       'belgaum', 'dehradun', 'rudrapur', 'jamshedpur', 'vizag', 'nouda',
       'dharamshala', 'banagalore', 'hissar', 'ranchi', 'madurai',
       'gurga', 'chandigarh', 'australia', 'cheyyar', 'sonepat',
       'ghaziabad', 'pantnagar', 'jagdalpur', 'angul', 'baroda',
       'ariyalur', 'jowai', 'kochi/cochin,chennaiandcoimbatore',
       'neemrana', 'tirupathi', 'bhubneshwar', 'calicut', 'dubai',
       'ahmednagar', 'nashik', 'bellary', 'ludhiana', 'muzaffarnagar',
       'gagret', 'indirapuram,ghaziabad', 'gwalior', 'chennai&mumbai',
       'rajasthan', 'sonipat', 'bareli', 'hospete', 'miryalaguda',
       'dharuhera', 'meerut', 'ganjam', 'hubli', 'ncr', 'agra', 'trichy',
       'kudankulam,tarapur', 'ongole', 'sambalpur', 'pondicherry',
       'bundi', 'sadulpur,rajgarh,distt-churu,rajasthan', 'am', 'bikaner',
       'vadodara', 'india', 'asansol', 'tirunelvelli', 'ernakulam',
       'bilaspur', 'chandrapur', 'nanded', 'dharmapuri', 'vandavasi',
       'rohtak', 'patna', 'salem', 'nasikcity', 'technopark,trivandrum',
       'bharuch', 'tornagallu', 'jaspur', 'burdwan', 'shimla',
       'gajiabaad', 'jammu', 'shahdol', 'muvattupuzha',
       'aljubail,saudiarabia', 'kalmar,sweden', 'secunderabad',
       'a-64,sec-64,noida', 'ratnagiri', 'jhajjar', 'gulbarga',
       'hyderabad(bhadurpally)', 'nalagarh', 'jeddahsaudiarabia',
       'chennai,bangalore', 'jamnagar', 'tirupati', 'gonda', 'orissa',
       'kharagpur', 'navimumbai,hyderabad', 'joshimath', 'bathinda',
       'johannesburg', 'kalaamb', 'karnal', 'london', 'kota', 'panchkula',
       'baddihp', 'nagari', 'mettur,tamilnadu', 'durgapur', 'pondi',
       'surat', 'kurnool', 'kolhapur', 'bhilai', 'hderabad',
       'bahadurgarh', 'rayagada,odisha', 'kakinada', 'varanasi', 'punr',
       'nellore', 'sahibabad', 'howrah', 'trichur', 'ambala', 'khopoli',
       'keral', 'roorkee', 'allahabad', 'delhi/ncr', 'jalandhar', 'vapi',
       'pilani', 'muzzafarpur', 'rasalkhaimah', 'bihar', 'singaruli',
       'pondy', 'phagwara', 'guragaon', 'baripada', 'yamunanagar',
       'shahibabad', 'sampla', 'guwahati', 'rourkela', 'banaglore',
       'vellore', 'dausa', 'latur(maharashtra)', 'mainpuri', 'dammam',
       'haldia', 'raebareli', 'patiala', 'gorakhpur', 'newdehli',
       'ambalacity', 'karad', 'rajpura', 'haryana', 'asifabadbanglore'],
      dtype=object)

Now we left with 230 Unique cities but still we can see dupplicate cities with little spelling mistake, and few cities with their old name. Lets change these city name one by one 🥱🥱

df['JobCity'].replace({"banglore":"bangalore","bengaluru":"bangalore","banagalore":"bangalore","banaglore":"bangalore","guragaon":"gurgaon",
                      "asifabadbanglore":"bangalore","chennai&mumbai":"chennai","greaternoida":"noida","bhubaneswar":"bhubaneshwar",
                      "chennai,bangalore":"chennai", "a-64,sec-64,noida":"noida", "nouda":"noida","vsakhapttnam":"visakhapatnam",
                      "kochi/cochin,chennaiandcoimbatore":"kochi","kochi/cochin":"kochi", "ncr":"delhi","trivandrum":"thiruvananthapuram",
                      "technopark,trivandrum":"thiruvananthapuram", "hderabad":"hyderabad","hyderabad(bhadurpally)":"hyderabad",
                      "ambalacity":"ambala", "newdehli":"newdelhi","punr":"pune","jeddahsaudiarabia":"jeddah-saudiarabia","dammam":"damman",
                      "aljubail,saudiarabia":"aljubail-saudiarabia","kalmar,sweden":"kalmar-sweden","kudankulam,tarapur":"kudankulam",
                      "latur(maharashtra)":"latur","delhi/ncr":"delhi","mettur,tamilnadu":"mettur-tamilnadu","nasikcity":"nashik",
                      "rayagada,odisha":"rayagada-odisha","damananddiu":"damman","indirapuram,ghaziabad":"ghaziabad","baroda":"vadodara",
                      "gajiabaad":"ghaziabad","gaziabaad":"ghaziabad","navimumbai,hyderabad":"navimumbai","bhubneshwar":"bhubaneshwar",
                      "sadulpur,rajgarh,distt-churu,rajasthan":"rajasthan","tirupathi":"tirupati","newdelhi-jaisalmer":"newdelhi"}, inplace=True)

df['JobCity'].nunique()

195

After cleaning the city names, we left with 195 Job Cities.

Convert these name to Title

df['JobCity']=df['JobCity'].str.title()
df['JobCity']

0       Bangalore
1          Indore
2         Chennai
3         Gurgaon
4         Manesar
          ...    
3993     Newdelhi
3994    Hyderabad
3995    Bangalore
3996    Bangalore
3997      Chennai
Name: JobCity, Length: 3998, dtype: object

In which City most employees are working

job_role=df['JobCity'].value_counts()[:20].reset_index()
plt.figure(figsize=(12,4))
sns.barplot(x='index', y='JobCity', data=job_role)
plt.xticks(rotation=60, fontsize=12)
plt.ylabel("Count of Job City")
plt.xlabel("Job City")
plt.title("Count of Enployees working in different City", fontsize=18, color='blue');

1. Most EMployees working in Bangalore.
2. Out of total nearly 20% of employees working in Bangalore.%
3. Bangalore, Noida, Hyderabad, Pune, Chennai are the top 5 Cities where most employees work.

Analyze Gender column

df['Gender'].dtype

dtype('O')

df['Gender'].unique()

array(['f', 'm'], dtype=object)

plt.figure(figsize=(12,4))
sns.countplot(df['Gender'], palette='pink_r')
plt.xticks(fontsize=15)
plt.title("Count of Enployees by Gender", fontsize=18, color='blue');

1. Count of Male employees is more than Female employees.
2. Nearly 80% Employees are Males.

Analyze Date Of Birth

df['DOB'].dtype

dtype('<M8[ns]')

Who is the Oldest and Youngest employee

To Answer this question we need present date and as per this dataset the present date can be assumed as 01-01-2016. Just Substract Date of Borth from Present Date i.e 01-01-2016. After Substracing We get TImeDelta result. By using datetime compoment we get the days from date of birth to now, by dividing this by 365 we get the Age of EMployee.

import datetime as dt

present='2016-01-01'
df['age_days']=(dt.datetime(2016, 1, 1) - df['DOB'])
df['age_days'].head(2)

0   9447 days
1   9585 days
Name: age_days, dtype: timedelta64[ns]

Now get the first component of timedelta object which is days and divide it by 365 to get year.

df['Age']=((df['age_days'].dt.components.iloc[:, 0])/365).astype(int)
df.drop('age_days', axis=1, inplace=True)
df['Age'].head(2)

0    25
1    26
Name: Age, dtype: int32

print("Yongest Employee DOB :", df['DOB'].min())
print("Oldest Employee DOB :", df['DOB'].max())

Yongest Employee DOB : 1977-10-30 00:00:00
Oldest Employee DOB : 1997-05-27 00:00:00

print("Yongest Employee Age :", df['Age'].min())
print("Oldest Employee Age :", df['Age'].max())
print("Average Age of Employees:", df['Age'].mean())

Yongest Employee Age : 18
Oldest Employee Age : 38
Average Age of Employees: 24.595547773886942

1. Oldest Employees Date of Birth is 30-10-1977, Youngest EMployees Date of Birth is 27-05-1997.
2. Oldest EMployees Age is 38 in the 2016 and Youngest Employees age is 18 in 2016.

Analyze Age column

df['Age'].dtype

dtype('int32')

What is the age range in which most of the employees are belongs to?

plt.figure(figsize=(12,4))
sns.countplot(df['Age'])
plt.xticks(fontsize=12)
plt.title("Count of Enployees by their Age", fontsize=18, color='blue');

1. Most of the employees are in the Age Range of 22 to 27.
2. Nearly 70% of total employees are of age 23,24 & 25.

Analyze 10th Percentage column

df['10percentage'].dtype

dtype('float64')

print("Minumum 10th Class Percentage :", df['10percentage'].min())
print("Maximum 10th Class Percentage :", df['10percentage'].max())
print("Average 10th Class Percentage: ", df['10percentage'].mean())

Minumum 10th Class Percentage : 43.0
Maximum 10th Class Percentage : 97.76
Average 10th Class Percentage:  77.9254427213606

Check the distribution of Percentage

plt.figure(figsize=(12,4))
sns.boxplot(df['10percentage'], color='pink')
plt.xticks(fontsize=12)
plt.title("Distribution of 10th Class Percentage of Employees", fontsize=18, color='blue');

plt.figure(figsize=(12,4))
sns.histplot(df['10percentage'], color='orange', kde=True)
plt.xticks(fontsize=12)
plt.title("Distribution of 10th Class Percentage of Employees", fontsize=18, color='blue');

1. Minumum 10th Class Percentage is 43%, Maximum 10th Class Percentage is 97.76% & Average 10th Class Percentage is 77.93%.
2. Most of the Employees has 10th Percetage between 70% to 90%.
3. There are few employees who's 10th Percentage is less than 50%.

Analyze 10th Board column

df['10board'].dtype

dtype('O')

df['10board'].nunique()

275

df['10board'].unique()

array(['board ofsecondary education,ap', 'cbse', 'state board',
       'mp board bhopal', 'icse',
       'karnataka secondary school of examination', 'up',
       'karnataka state education examination board', 'ssc',
       'kerala state technical education', 0, 'bseb',
       'state board of secondary education, andhra pradesh',
       'matriculation', 'gujarat state board', 'karnataka state board',
       'wbbse', 'maharashtra state board', 'icse board', 'up board',
       'board of secondary education(bse) orissa',
       'little jacky matric higher secondary school',
       'uttar pradesh board', 'bsc,orissa', 'mp board', 'upboard',
       'matriculation board', 'j & k bord', 'rbse',
       'central board of secondary education', 'pseb', 'jkbose',
       'haryana board of school education,(hbse)', 'metric', 'ms board',
       'kseeb', 'stateboard', 'maticulation',
       'karnataka secondory education board', 'mumbai board', 'sslc',
       'kseb', 'board secondary  education', 'matric board',
       'board of secondary education',
       'west bengal board of secondary education',
       'jharkhand secondary examination board,ranchi', 'u p board',
       'bseb,patna', 'hsc', 'bse', 'sss pune',
       'karnataka education board (keeb)', 'kerala',
       'state board of secondary education( ssc)', 'gsheb',
       'up(allahabad)', 'nagpur', 'don bosco maatriculation school',
       'karnataka state secondary education board', 'maharashtra',
       'karnataka secondary education board',
       'himachal pradesh board of school education',
       'certificate of middle years program of ib',
       'karnataka board of secondary education',
       'board of secondary education rajasthan', 'uttarakhand board',
       'ua', 'board of secendary education orissa',
       'karantaka secondary education and examination borad', 'hbsc',
       'kseeb(karnataka secondary education examination board)',
       'cbse[gulf zone]', 'hbse', 'state(karnataka board)',
       'jharkhand accademic council',
       'jharkhand secondary examination board (ranchi)',
       'karnataka secondary education examination board', 'delhi board',
       'mirza ahmed ali baig', 'jseb', 'bse, odisha', 'bihar board',
       'maharashtra state(latur board)', 'rajasthan board', 'mpboard',
       'upbhsie', 'secondary board of rajasthan',
       'tamilnadu matriculation board', 'jharkhand secondary board',
       'board of secondary education,andhara pradesh', 'up baord',
       'state', 'board of intermediate education',
       'state board of secondary education,andhra pradesh',
       'up board , allahabad',
       'stjosephs girls higher sec school,dindigul', 'maharashtra board',
       'education board of kerala', 'board of ssc',
       'maharashtra state board pune',
       'board of school education harayana',
       'secondary school cerfificate', 'maharashtra sate board', 'ksseb',
       'bihar examination board, patna', 'latur',
       'board of secondary education, rajasthan', 'state borad hp',
       'cluny', 'bsepatna', 'up borad', 'ssc board of andrapradesh',
       'matric', 'bse,orissa', 'ssc-andhra pradesh', 'mp',
       'karnataka education board', 'mhsbse',
       'karnataka sslc board bangalore', 'karnataka', 'u p',
       'secondary school of education', 'state board of karnataka',
       'karnataka secondary board', 'andhra pradesh board ssc',
       'stjoseph of cluny matrhrsecschool,neyveli,cuddalore district',
       'hse,orissa', 'national public school', 'nagpur board',
       'jharkhand academic council', 'bsemp',
       'board of secondary education, andhra pradesh',
       'board of secondary education orissa',
       'board of secondary education,rajasthan(rbse)',
       'board of secondary education,ap',
       'board of secondary education,andhra pradesh',
       'jawahar navodaya vidyalaya', 'aisse',
       'karnataka board of higher education', 'bihar',
       'kerala state board', 'cicse', 'tn state board',
       'kolhapur divisional board, maharashtra',
       'bharathi matriculation school', 'uttaranchal state board',
       'wbbsce', 'mp state board', 'seba(assam)', 'anglo indian', 'gseb',
       'uttar pradesh', 'ghseb', 'board of school education uttarakhand',
       'msbshse,pune', 'tamilnadu state board', 'kerala university',
       'uttaranchal shiksha avam pariksha parishad',
       'bse(board of secondary education)',
       'bright way college, (up board)',
       'school secondary education, andhra pradesh',
       'secondary state certificate',
       'maharashtra state board of secondary and higher secondary education,pune',
       'andhra pradesh state board', 'stmary higher secondary', 'cgbse',
       'secondary school certificate', 'rajasthan board ajmer', 'mpbse',
       'pune board', 'cbse ', 'board of secondary education,orissa',
       'maharashtra state board,pune', 'up bord',
       'kiran english medium high school', 'state board (jac, ranchi)',
       'gujarat board', 'state board ', 'sarada high scchool',
       'kalaimagal matriculation higher secondary school',
       'karnataka board', 'maharastra board', 'sslc board',
       'ssc maharashtra board', 'tamil nadu state', 'uttrakhand board',
       'bihar secondary education board,patna',
       'haryana board of school education',
       'sri kannika parameswari highier secondary school, udumalpet',
       'ksseb(karnataka state board)', 'nashik board',
       'jharkhand secondary education board', 'himachal pradesh board',
       'maharashtra satate board',
       'maharashtra state board mumbai divisional board',
       'dav public school,hehal',
       'state board of secondary education, ap',
       'rajasthan board of secondary education', 'hsce',
       'karnataka secondary education',
       'board of secondary education,odisha', 'maharashtra nasik board',
       'west bengal board of secondary examination (wbbse)',
       'holy cross matriculation hr sec school', 'cbsc', 'apssc',
       'bseb patna', 'kolhapur', 'bseb, patna', 'up board allahabad',
       'biharboard', 'nagpur board,nagpur', 'pune', 'gyan bharati school',
       'rbse,ajmer', 'board of secondaray education',
       'secondary school education', 'state bord', 'jbse,jharkhand',
       'hse', 'madhya pradesh board', 'bihar school examination board',
       'west bengal board of secondary eucation', 'state boardmp board ',
       'icse board , new delhi',
       'board of secondary education (bse) orissa',
       'maharashtra state board for ssc',
       'board of secondary school education', 'latur board',
       "stmary's convent inter college", 'nagpur divisional board',
       'ap state board', 'cgbse raipur', 'uttranchal board', 'ksbe',
       'central board of secondary education, new delhi',
       'bihar school examination board patna', 'cbse board',
       'sslc,karnataka', 'mp-bse', 'up bourd', 'dav public school sec 14',
       'board of school education haryana',
       'council for indian school certificate examination',
       'aurangabad board', 'j&k state board of school education',
       'maharashtra state board of secondary and higher secondary education',
       'maharashtra state boar of secondary and higher secondary education',
       'ssc regular', 'karnataka state examination board', 'nasik',
       'west bengal  board of secondary education', 'up board,allahabad',
       'bseb ,patna',
       'state board - west bengal board of secondary education : wbbse',
       'maharashtra state board of secondary & higher secondary education',
       'delhi public school', 'karnataka secondary eduction',
       'secondary education board of rajasthan',
       'maharashtra board, pune', 'rbse (state board)', 'apsche',
       'board of  secondary education',
       'board of high school and intermediate education uttarpradesh',
       'kea', 'board of secondary education - andhra pradesh',
       'ap state board for secondary education', 'seba',
       'punjab school education board, mohali',
       'jharkhand acedemic council', 'hse,board',
       'board of ssc education andhra pradesh', 'up-board', 'bse,odisha'],
      dtype=object)

10th Education boards are repeating with full form and some are repeating with abbreaviation. Lets clean this column. Here I am changing the Any state board name to state board.

df['10board']=df['10board'].str.strip()

def change_10thboard_name(name):
    if name not in ['cbse', 'icse board', 'cbse board', 'icse', 'cisce', 'cbse[gulf zone]']:
        return 'State Board'
    elif name in ['cbse','cbse[gulf zone]','cbse board']:
        return 'CBSE'
    elif name in ['icse', 'icse board','cisce']:
        return 'ICSE'
    
df['10board']=df['10board'].apply(change_10thboard_name)
df['10board'].unique()

array(['State Board', 'CBSE', 'ICSE'], dtype=object)

Check the count of Employees by 10th Board

plt.figure(figsize=(12,4))
sns.countplot(df['10board'])
plt.xticks(fontsize=12)
plt.title("Count of Employees by their 10th Board", fontsize=18, color='blue');

1. This is Expected becuase lots of student went to their respective State boards only.
2. Nearly 60% Employees studied in Respective State Boards.
3. Nearly 35-38% EMployees studied in 'CBSE Board.
4. 2-3% studied in ICSE Board.

Analyze 12th Passing year

df['12graduation'].dtype

dtype('int64')

df['12graduation'].nunique()

16

plt.figure(figsize=(12,4))
sns.countplot(df['12graduation'])
plt.xticks(fontsize=12)
plt.title("Count of Employees by their 12th Pass Year", fontsize=18, color='blue');

1. Most of Employees Passed out 12th in year 2009.
2. Count of 12th Pass employees is more in 2006 to 2010.

Analyze 12th Percentage column

df['12percentage'].dtype

dtype('float64')

print("Minumum 12th Class Percentage :", df['12percentage'].min())
print("Maximum 12th Class Percentage :", df['12percentage'].max())
print("Average 12th Class Percentage: ", df['12percentage'].mean())

Minumum 12th Class Percentage : 40.0
Maximum 12th Class Percentage : 98.7
Average 12th Class Percentage:  74.46636568284127

Check the distribution of Percentage

plt.figure(figsize=(12,4))
sns.boxplot(df['12percentage'], color='pink')
plt.xticks(fontsize=12)
plt.title("Distribution of 12th Class Percentage of Employees", fontsize=18, color='blue');

plt.figure(figsize=(12,4))
sns.histplot(df['12percentage'], color='orange', kde=True)
plt.xticks(fontsize=12)
plt.title("Distribution of 12th Class Percentage of Employees", fontsize=18, color='blue');

1. Minumum 12th Class Percentage is 40%, Maximum 10th Class Percentage is 98.7% & Average 10th Class Percentage is 78.46%.
2. Most of the Employees has 12th Percetage between 60% to 90%.
3. There are few employees who's 12th Percentage is less than 50%. and there is one employee whos percentage is 40%

Analyze 12th Board column

df['12board'].dtype

dtype('O')

df['12board'].nunique()

340

df['12board'].unique()

array(['board of intermediate education,ap', 'cbse', 'state board',
       'mp board', 'isc', 'icse', 'karnataka pre university board', 'up',
       'p u board, karnataka', 'dept of pre-university education', 'bie',
       'kerala state hse board', 'up board', 0, 'bseb', 'chse', 'puc',
       ' upboard',
       'state  board of intermediate education, andhra pradesh',
       'karnataka state board',
       'west bengal state council of technical education', 'wbchse',
       'maharashtra state board', 'ssc', 'isc board',
       'sda matric higher secondary school', 'uttar pradesh board', 'ibe',
       'chsc', 'board of intermediate', 'isce', 'upboard', 'sbtet',
       'hisher seconadry examination(state board)', 'pre university',
       'borad of intermediate', 'j & k board',
       'intermediate board of andhra pardesh', 'rbse',
       'central board of secondary education', 'jkbose', 'hbse',
       'board of intermediate education', 'state', 'ms board', 'pue',
       'intermediate state board', 'stateboard', 'hsc',
       'electonincs and communication(dote)', 'karnataka pu board',
       'government polytechnic mumbai , mumbai board', 'pu board',
       'baord of intermediate education', 'apbie', 'andhra board',
       'tamilnadu stateboard',
       'west bengal council of higher secondary education',
       'cbse,new delhi', 'u p board', 'intermediate', 'biec,patna',
       'diploma in engg (e &tc) tilak maharashtra vidayapeeth',
       'hsc pune', 'pu board karnataka', 'kerala', 'gsheb',
       'up(allahabad)', 'nagpur', 'st joseph hr sec school',
       'pre university board', 'ipe', 'maharashtra', 'kea', 'apsb',
       'himachal pradesh board of school education', 'staae board',
       'international baccalaureate (ib) diploma', 'nios',
       'karnataka board of university',
       'board of secondary education rajasthan', 'uttarakhand board',
       'ua', 'scte vt orissa', 'matriculation',
       'department of pre-university education', 'wbscte',
       'preuniversity board(karnataka)', 'jharkhand accademic council',
       'bieap', 'msbte (diploma in computer technology)',
       'jharkhand acamedic council (ranchi)',
       'department of pre-university eduction', 'biec', 'all india board',
       'sjrcw', ' board of intermediate', 'msbte',
       'sri sankara vidyalaya', 'chse, odisha', 'bihar board',
       'maharashtra state(latur board)', 'rajasthan board', 'mpboard',
       'state board of technical eduction panchkula', 'upbhsie', 'apbsc',
       'state board of technical education and training',
       'secondary board of rajasthan',
       'tamilnadu higher secondary education board',
       'jharkhand academic council',
       'board of intermediate education,hyderabad', 'up baord', 'pu',
       'dte', 'board of secondary education', 'pre-university',
       'board of intermediate education,andhra pradesh',
       'up board , allahabad', 'srv girls higher sec school,rasipuram',
       'intermediate board of education,andhra pradesh',
       'intermediate board examination',
       'department of pre-university education, bangalore',
       'stmiras college for girls', 'mbose',
       'department of pre-university education(government of karnataka)',
       'dpue', 'msbte pune', 'board of school education harayana',
       'sbte, jharkhand', 'bihar intermediate education council, patna',
       'higher secondary', 's j polytechnic', 'latur',
       'board of secondary education, rajasthan', 'jyoti nivas', 'pseb',
       'biec-patna', 'board of intermediate education,andra pradesh',
       'chse,orissa', 'pre-university board', 'mp', 'intermediate board',
       'govt of karnataka department of pre-university education',
       'karnataka education board',
       'board of secondary school of education', 'pu board ,karnataka',
       'karnataka secondary education board', 'karnataka sslc',
       'board of intermediate ap', 'u p', 'state board of karnataka',
       'directorate of technical education,banglore', 'matric board',
       'andhpradesh board of intermediate education',
       'stjoseph of cluny matrhrsecschool,neyveli,cuddalore district',
       'bte up', 'scte and vt ,orissa', 'hbsc',
       'jawahar higher secondary school', 'nagpur board', 'bsemp',
       'board of intermediate education, andhra pradesh',
       'board of higher secondary orissa',
       'board of secondary education,rajasthan(rbse)',
       'board of intermediate education:ap,hyderabad', 'science college',
       'karnatak pu board', 'aissce', 'pre university board of karnataka',
       'bihar', 'kerala state board', 'uo board', 'cicse',
       'karnataka board', 'tn state board',
       'kolhapur divisional board, maharashtra',
       'jaycee matriculation school',
       'board of higher secondary examination, kerala',
       'uttaranchal state board', 'intermidiate', 'bciec,patna', 'bice',
       'karnataka state', 'state broad', 'wbbhse', 'gseb',
       'uttar pradesh', 'ghseb', 'board of school education uttarakhand',
       'gseb/technical education board', 'msbshse,pune',
       'tamilnadu state board', 'board of technical education',
       'kerala university', 'uttaranchal shiksha avam pariksha parishad',
       'chse(concil of higher secondary education)',
       'bright way college, (up board)', 'board of intermidiate',
       'higher secondary state certificate', 'karanataka secondary board',
       'maharashtra board', 'andhra pradesh state board', 'cgbse',
       'diploma in computers', 'bte,delhi', 'rajasthan board ajmer',
       'mpbse', 'pune board', 'state board of technical education',
       'gshseb', 'amravati divisional board',
       'dote (diploma - computer engg)', 'up bord',
       'karnataka pre-university board', 'jharkhand board',
       'punjab state board of technical education & industrial training',
       'department of technical education',
       'sri chaitanya junior kalasala', 'state board (jac, ranchi)',
       'gujarat board', 'aligarh muslim university',
       'tamil nadu state board', 'hse', 'karnataka secondary education',
       'state board ', 'karnataka pre unversity board',
       'ks rangasamy institute of technology',
       'karnataka board secondary education', 'narayana junior college',
       'bteup', 'board of intermediate(bie)', 'hsc maharashtra board',
       'tamil nadu state', 'uttrakhand board', 'psbte',
       'stateboard/tamil nadu', 'intermediate council patna',
       'technical board, punchkula', 'board of intermidiate examination',
       'sri kannika parameswari highier secondary school, udumalpet',
       'ap board', 'nashik board', 'himachal pradesh board',
       'maharashtra satate board',
       'andhra pradesh board of secondary education',
       'tamil nadu polytechnic',
       'maharashtra state board mumbai divisional board',
       'department of pre university education',
       'dav public school,hehal', 'board of intermediate education, ap',
       'rajasthan board of secondary education',
       'department of technical education, bangalore', 'chse,odisha',
       'maharashtra nasik board',
       'west bengal council of higher secondary examination (wbchse)',
       'holy cross matriculation hr sec school', 'cbsc',
       'pu  board karnataka', 'biec patna', 'kolhapur', 'bseb, patna',
       'up board allahabad', 'intermideate', 'nagpur board,nagpur',
       'diploma(msbte)', 'dav public school',
       'pre university board, karnataka', 'ssm srsecschool', 'state bord',
       'jstb,jharkhand', 'intermediate board of education',
       'mp board bhopal', 'pub', 'madhya pradesh board',
       'bihar intermediate education council',
       'west bengal council of higher secondary eucation',
       'isc board , new delhi', 'mpc',
       'certificate for higher secondary education (chse)orissa',
       'maharashtra state board for hsc',
       'board of intermeadiate education', 'latur board',
       'andhra pradesh', 'karnataka pre-university',
       'lucknow public college', 'nagpur divisional board',
       'ap intermediate board', 'cgbse raipur', 'uttranchal board',
       'jiec', 'central board of secondary education, new delhi',
       'bihar school examination board patna',
       'state board of technical education harayana', 'mp-bse',
       'up bourd', 'dav public school sec 14',
       'haryana state board of technical education chandigarh',
       'council for indian school certificate examination',
       'jaswant modern school', 'madhya pradesh open school',
       'aurangabad board', 'j&k state board of school education',
       'diploma ( maharashtra state board of technical education)',
       'board of technicaleducation ,delhi',
       'maharashtra state boar of secondary and higher secondary education',
       'hslc (tamil nadu state board)',
       'karnataka state examination board', 'puboard', 'nasik',
       'west bengal board of higher secondary education',
       'up board,allahabad', 'board of intrmediate education,ap', 'cbese',
       'karnataka state pre- university board',
       'state board - west bengal council of higher secondary education : wbchse',
       'maharashtra state board of secondary & higher secondary education',
       'biec, patna', 'state syllabus', 'cbse board', 'scte&vt',
       'board of intermediate,ap',
       'secnior secondary education board of rajasthan',
       'maharashtra board, pune', 'rbse (state board)',
       'board of intermidiate education,ap',
       'board of high school and intermediate education uttarpradesh',
       'higher secondary education',
       'board fo intermediate education, ap', 'intermedite',
       'ap board for intermediate education', 'ahsec',
       'punjab state board of technical education & industrial training, chandigarh',
       'state board - tamilnadu', 'jharkhand acedemic council',
       'scte & vt (diploma)', 'karnataka pu',
       'board of intmediate education ap', 'up-board',
       'boardofintermediate'], dtype=object)

Lets rename every respective state board to "State Board" and rest are "CBSE","ICSE".

df['10board']=df['10board'].str.strip()

def change_12thboard_name(name):
    if name not in ['cbse', 'icse board', 'cbse board', 'icse', 'cisce', 'cbse[gulf zone]']:
        return 'State Board'
    elif name in ['cbse','cbse[gulf zone]','cbse board']:
        return 'CBSE'
    elif name in ['icse', 'icse board','cisce']:
        return 'ICSE'
    
df['12board']=df['12board'].apply(change_12thboard_name)
df['12board'].unique()

array(['State Board', 'CBSE', 'ICSE'], dtype=object)

plt.figure(figsize=(12,4))
sns.countplot(df['12board'])
plt.xticks(fontsize=12)
plt.title("Count of Employees by their 12th Board", fontsize=18, color='blue');

1. Nearly 60% Employees studied in Respective State Boards.
2. Nearly 38% EMployees studied in 'CBSE Board.
3. 1-2% studied in ICSE Board.

Analyze College Tier

df['CollegeTier'].dtype

dtype('int64')

df['CollegeTier'].unique()

array([2, 1], dtype=int64)

How many employees belongs to each College Tier

plt.figure(figsize=(12,4))
sns.countplot(df['CollegeTier'], palette='pink_r')
plt.xticks(fontsize=12)
plt.title("Count of Employees by their College Tier", fontsize=18, color='blue');

90% Employeess are from 2nd College Tier.

Analyze Degree column

df['Degree'].dtype

dtype('O')

df['Degree'].unique()

array(['B.Tech/B.E.', 'MCA', 'M.Tech./M.E.', 'M.Sc. (Tech.)'],
      dtype=object)

Count of employees by Degree

df['Degree'].value_counts()

B.Tech/B.E.      3700
MCA               243
M.Tech./M.E.       53
M.Sc. (Tech.)       2
Name: Degree, dtype: int64

plt.figure(figsize=(12,4))
sns.countplot(df['Degree'])
plt.xticks(fontsize=12)
plt.title("Count of Employees by Degree", fontsize=18, color='blue');

1. 90% Employees are from Engineering discipline (B.E/B.Tech & ME/M.Tech)
2. 5-6% are from MCA
3. Only 2 employees are from MSc.

Analyze Specialization or Branch column

df['Specialization'].dtype

dtype('O')

df['Specialization'].unique()

array(['computer engineering',
       'electronics and communication engineering',
       'information technology', 'computer science & engineering',
       'mechanical engineering', 'electronics and electrical engineering',
       'electronics & telecommunications',
       'instrumentation and control engineering', 'computer application',
       'electronics and computer engineering', 'electrical engineering',
       'applied electronics and instrumentation',
       'electronics & instrumentation eng',
       'information science engineering', 'civil engineering',
       'mechanical and automation', 'industrial & production engineering',
       'control and instrumentation engineering',
       'metallurgical engineering',
       'electronics and instrumentation engineering',
       'electronics engineering', 'ceramic engineering',
       'chemical engineering', 'aeronautical engineering', 'other',
       'biotechnology', 'embedded systems technology',
       'electrical and power engineering',
       'computer science and technology', 'mechatronics',
       'automobile/automotive engineering', 'polymer technology',
       'mechanical & production engineering',
       'power systems and automation', 'instrumentation engineering',
       'telecommunication engineering',
       'industrial & management engineering', 'industrial engineering',
       'computer and communication engineering',
       'information & communication technology', 'information science',
       'internal combustion engine', 'computer networking',
       'biomedical engineering', 'electronics', 'computer science'],
      dtype=object)

Many Branch Names repeated here, lets change them to Proper format.

def change_branch_names(branch):
    if branch in ['computer science','computer science and technology', 'computer and communication engineering',
                  'computer science & engineering','computer engineering','computer networking']:
        return "Computer Science Engineering"
    elif branch in ['electronics','electronics engineering','electronics and communication engineering',
                    'electronics and computer engineering']:
        return "Electronics & Communication Engineering"
    elif branch in ['electronics & instrumentation eng','electronics & instrumentation eng','instrumentation engineering',
                    'control and instrumentation engineering','electronics and instrumentation engineering',
                    'instrumentation and control engineering','applied electronics and instrumentation']:
        return "Electronics & Instrumentation Engineering"
    elif branch in ['electronics & telecommunications','telecommunication engineering',]:
        return "Electronics & Telecommunication Engineering"
    elif branch in ['information & communication technology', 'information science','information technology',
                    'information science engineering']:
        return "Information Technology/Science"
    elif branch in ['electronics and electrical engineering','electrical and power engineering']:
        return "Electrical Engineering"
    elif branch in ['industrial & management engineering', 'industrial engineering']:
        return "Industrial Engineering"
    elif branch in ['internal combustion engine','mechanical & production engineering','mechanical and automation','mechatronics']:
        return "Mechanical Engineering"
    else:
        return branch
    
df['Specialization']=df['Specialization'].apply(change_branch_names)

#Making Specialization in title format
df['Specialization']=df['Specialization'].str.title()

df['Specialization'].unique()

array(['Computer Science Engineering',
       'Electronics & Communication Engineering',
       'Information Technology/Science', 'Mechanical Engineering',
       'Electrical Engineering',
       'Electronics & Telecommunication Engineering',
       'Electronics & Instrumentation Engineering',
       'Computer Application', 'Civil Engineering',
       'Industrial & Production Engineering', 'Metallurgical Engineering',
       'Ceramic Engineering', 'Chemical Engineering',
       'Aeronautical Engineering', 'Other', 'Biotechnology',
       'Embedded Systems Technology', 'Automobile/Automotive Engineering',
       'Polymer Technology', 'Power Systems And Automation',
       'Industrial Engineering', 'Biomedical Engineering'], dtype=object)

Count of Employees by Specialization

plt.figure(figsize=(12,8))
sns.countplot(y=df['Specialization'])
plt.xticks(fontsize=12)
plt.title("Count of Employees by Specialization/Branch", fontsize=18, color='blue');

1. Most Employees belongs to Computer Science, Electronics & Communication, Information Technology, Mechanical, Electrical, Electronics & Telecommunication, Electronics & Instrumentation and Computer Application.
2. CS,EC,IT/IS are more likely to employed in Software Field.

Anlyze College GPA columns

df['collegeGPA'].dtype

dtype('float64')

plt.figure(figsize=(12,4))
sns.histplot(df['collegeGPA'], color='orange', kde=True)
plt.xticks(fontsize=12)
plt.title("Distribution of College GPA", fontsize=18, color='blue');

In some Universities, GPA is calculated on 10th Scale, Lets convert these value to 100th Scale.

def gpa_conversion(gpa):
    if gpa<10:
        return gpa*10
    else:
        return gpa

df['collegeGPA']=df['collegeGPA'].apply(gpa_conversion)

print("Highest GPA:",df['collegeGPA'].max())
print("Lowest GPA:",df['collegeGPA'].min())
print("Average GPA:",df['collegeGPA'].mean())

Highest GPA: 99.93
Lowest GPA: 49.07
Average GPA: 71.69572786393196

plt.figure(figsize=(12,4))
sns.boxplot(df['collegeGPA'], color='pink')
plt.xticks(fontsize=12)
plt.title("Distribution of College GPA", fontsize=18, color='blue');

plt.figure(figsize=(12,4))
sns.histplot(df['collegeGPA'], color='orange', kde=True)
plt.xticks(fontsize=12)
plt.title("Distribution of College GPA", fontsize=18, color='blue');

1. Minimum College GPA is 49%, Maximum is 99.93% and Average GPA is 71.70%.
2. Most of the employees have GPA between 60%-90%.
3. There are No extreame Outliers in the GPA column.

Analyze College City Tier: Location in which the college locted in City

df['CollegeCityTier'].dtype

dtype('int64')

df['CollegeCityTier'].unique()

array([0, 1], dtype=int64)

plt.figure(figsize=(12,4))
sns.countplot(y= df['CollegeCityTier'], palette='pink_r')
plt.xticks(fontsize=12)
plt.title("College City Tier", fontsize=18, color='blue');

Most of the Colleges Located in the 0th Tier of the city.

Analyze College State

df['CollegeState'].dtype

dtype('O')

df['CollegeState'].unique()

array(['Andhra Pradesh', 'Madhya Pradesh', 'Uttar Pradesh', 'Delhi',
       'Karnataka', 'Tamil Nadu', 'West Bengal', 'Maharashtra', 'Haryana',
       'Telangana', 'Orissa', 'Punjab', 'Kerala', 'Gujarat', 'Rajasthan',
       'Chhattisgarh', 'Uttarakhand', 'Jammu and Kashmir', 'Jharkhand',
       'Himachal Pradesh', 'Bihar', 'Assam', 'Goa', 'Sikkim',
       'Union Territory', 'Meghalaya'], dtype=object)

plt.figure(figsize=(12,4))
sns.countplot(df['CollegeState'])
plt.xticks(fontsize=12, rotation=70)
plt.title("Count of the Employees by their College State", fontsize=18, color='blue');

1. Highest Number of Employees studied from Uttar Pradesh College's.
2. Large number of Employees are belongs to College of State Uttar Pradesh, Karnataka, Tamil Nadu, Telangana, Maharastra.
3. Least number of EMployees are belongs to College of State Assam, Goa, Sikkim, Meghalaya and Unioin Teretories.

Analyze Bachelors Degree Graduation Year of Employees

df['GraduationYear'].dtype

dtype('int64')

df['GraduationYear'].unique()

array([2011, 2012, 2014, 2016, 2013, 2010, 2015, 2009, 2017,    0, 2007],
      dtype=int64)

There is one unique entry in Graduation Year which "0", lets see what it is.

df[df['GraduationYear']==0]

	ID	Salary	DOJ	DOL	Designation	JobCity	Gender	DOB	10percentage	10board	12graduation	12percentage	12board	CollegeID	CollegeTier	Degree	Specialization	collegeGPA	CollegeCityID	CollegeCityTier	CollegeState	GraduationYear	English	Logical	Quant	Domain	ComputerProgramming	ElectronicsAndSemicon	ComputerScience	MechanicalEngg	ElectricalEngg	TelecomEngg	CivilEngg	conscientiousness	agreeableness	extraversion	nueroticism	openess_to_experience	Year_of_Joining	Year_of_Leaving	Age
2664	794381	325000	2014-07-01	2015-07-01	System Engineer	Chandigarh	m	1991-12-17	89.0	CBSE	2010	85.2	CBSE	8346	2	B.Tech/B.E.	Mechanical Engineering	68.76	8346	0	Uttar Pradesh	0	695	655	680	0.755375	-1	-1	-1	446	-1	-1	-1	1.1336	0.3789	-0.2974	-0.3612	-1.244	2014	2015	24

This Employees 12th Passing year is 2010 and he studied 'Engineering' so my logically his graduation year must be 2014.

df['GraduationYear'].replace({0:2014}, inplace=True)
df['GraduationYear'].unique()

array([2011, 2012, 2014, 2016, 2013, 2010, 2015, 2009, 2017, 2007],
      dtype=int64)

plt.figure(figsize=(12,4))
sns.countplot(df['GraduationYear'])
plt.xticks(fontsize=12)
plt.title("Count of the Employees by their Bachelors Degree Graduation Year", fontsize=18, color='blue');

Highest Number of Employees are completed their Bachelors Degree in year 2013 and least in 2007.

Analyze Scores in AMCAT English,Logical,Quantitative and Domain section

df[['English','Logical','Quant','Domain']].dtypes

English      int64
Logical      int64
Quant        int64
Domain     float64
dtype: object

Distribution of Scores

fig,ax=plt.subplots(2,2, figsize=(15,6))
sns.boxplot(df['English'], color='pink', ax=ax[0][0]).set(title='Distribution Scores in English')
sns.boxplot(df['Logical'], color='orange', ax=ax[0][1]).set(title='Distribution of Scores in Logical')
sns.boxplot(df['Quant'], color='blue', ax=ax[1][0]).set(title='Distribution of Scores in  Quantitative')
sns.boxplot(df['Domain'], color='green', ax=ax[1][1]).set(title='Distribution of  Scores in Domain');

fig,ax=plt.subplots(2,2, figsize=(15,6))
sns.histplot(df['English'],kde=True, color='pink', ax=ax[0][0]).set(title='Distribution Scores in English')
sns.histplot(df['Logical'],kde=True, color='orange', ax=ax[0][1]).set(title='Distribution of Scores in Logical')
sns.histplot(df['Quant'],kde=True, color='blue', ax=ax[1][0]).set(title='Distribution of Scores in  Quantitative')
sns.histplot(df['Domain'],kde=True, color='green', ax=ax[1][1]).set(title='Distribution of  Scores in Domain');

1. Some of employees scores very well in exams, Some are extra-ordiary.
2. In the Domain Score, there are some scores=-1, this means either they did not attended the Domain Exam or they do not have any specific doamin.

Minimum, Maximum Scores in All Subject.

print("Minimum Score in English={}, Logical={}, Quantitative={}, Domain={}".format(df['English'].min(),df['Logical'].min(), df['Quant'].min(), df['Domain'].min()))
print("Maximum Score in English={}, Logical={}, Quantitative={}, Domain={}".format(df['English'].max(),df['Logical'].max(), df['Quant'].max(), df['Domain'].max()))
print("Average Score in English={}, Logical={}, Quantitative={}, Domain={}".format(df['English'].mean(),df['Logical'].mean(), df['Quant'].mean(), df['Domain'].mean()))

Minimum Score in English=180, Logical=195, Quantitative=120, Domain=-1.0
Maximum Score in English=875, Logical=795, Quantitative=900, Domain=0.999910407631314
Average Score in English=501.64907453726863, Logical=501.59879939969983, Quantitative=513.3781890945472, Domain=0.5104896530075451

Average Score in all the Subjects is in range 500 to 510.

Analyze Score of Computer Programming, Electronics And Semiconductor, Computer Science, Mechanical Engg, ElectricalEngg, TelecomEngg and CivilEngg.

df[['ComputerProgramming','ElectronicsAndSemicon','ComputerScience','MechanicalEngg','ElectricalEngg','TelecomEngg','CivilEngg']].dtypes

ComputerProgramming      int64
ElectronicsAndSemicon    int64
ComputerScience          int64
MechanicalEngg           int64
ElectricalEngg           int64
TelecomEngg              int64
CivilEngg                int64
dtype: object

Distribution of Core Domain Scores

There are some Employees for who the Domain is Different, Scores are allotted for those columns where the employee has taken the exam. for other that the specific doamin column the scores=-1, lets see the distribution for only the score>=0

fig,ax=plt.subplots(4,2, figsize=(18,20))
sns.boxplot(df[df['ComputerProgramming']>=0].ComputerProgramming, color='pink', ax=ax[0][0]).set(title='Distribution Scores in Computer Programming')
sns.boxplot(df[df['ElectronicsAndSemicon']>=0].ElectronicsAndSemicon, color='orange', ax=ax[0][1]).set(title='Distribution of Scores in Electronics And Semicon')
sns.boxplot(df[df['ComputerScience']>=0].ComputerScience, color='blue', ax=ax[1][0]).set(title='Distribution of Scores in  Computer Science')
sns.boxplot(df[df['MechanicalEngg']>=0].MechanicalEngg, color='green', ax=ax[1][1]).set(title='Distribution of  Scores in Mechanical Engineering')
sns.boxplot(df[df['ElectricalEngg']>=0].ElectricalEngg, color='yellow', ax=ax[2][0]).set(title='Distribution of Scores in Electrical Engineering')
sns.boxplot(df[df['TelecomEngg']>=0].TelecomEngg, color='lightblue', ax=ax[2][1]).set(title='Distribution of Scores in  Telecom Engineering')
sns.boxplot(df[df['CivilEngg']>=0].CivilEngg, color='gray', ax=ax[3][0]).set(title='Distribution of  Scores in Civil Engineering');

fig,ax=plt.subplots(4,2, figsize=(18,20))
sns.histplot(df[df['ComputerProgramming']>=0].ComputerProgramming, kde=True, color='pink', ax=ax[0][0]).set(title='Distribution Scores in Computer Programming')
sns.histplot(df[df['ElectronicsAndSemicon']>=0].ElectronicsAndSemicon, kde=True, color='orange', ax=ax[0][1]).set(title='Distribution of Scores in Electronics And Semicon')
sns.histplot(df[df['ComputerScience']>=0].ComputerScience, kde=True, color='blue', ax=ax[1][0]).set(title='Distribution of Scores in  Computer Science')
sns.histplot(df[df['MechanicalEngg']>=0].MechanicalEngg, kde=True, color='green', ax=ax[1][1]).set(title='Distribution of  Scores in Mechanical Engineering')
sns.histplot(df[df['ElectricalEngg']>=0].ElectricalEngg, kde=True, color='yellow', ax=ax[2][0]).set(title='Distribution of Scores in Electrical Engineering')
sns.histplot(df[df['TelecomEngg']>=0].TelecomEngg, kde=True, color='lightblue', ax=ax[2][1]).set(title='Distribution of Scores in  Telecom Engineering')
sns.histplot(df[df['CivilEngg']>=0].CivilEngg, kde=True, color='gray', ax=ax[3][0]).set(title='Distribution of  Scores in Civil Engineering');

1. In the Computer Programming Exam Job Swwkers have score above 800 Marks, but rest of the Exams the Maximum score is very less
2. We can say that there is more Competetion in the field of Computer Programming.

Average, Minimum and Maximum scores is all Specific Domain Exams

for col in ['ComputerProgramming','ElectronicsAndSemicon','ComputerScience','MechanicalEngg','ElectricalEngg','TelecomEngg','CivilEngg']:
    print(f'Subject: {col}')
    print(f'Minimum Score:{(df[df[col]>=0][col]).min()}', end=" ")
    print(f'Maximum Score:{(df[df[col]>=0][col]).max()}', end=" ")
    print(f'Average Score:{(df[df[col]>=0][col]).mean()}')
    print("--"*10)

Subject: ComputerProgramming
Minimum Score:105 Maximum Score:840 Average Score:451.3012779552716
--------------------
Subject: ElectronicsAndSemicon
Minimum Score:133 Maximum Score:612 Average Score:335.6442307692308
--------------------
Subject: ComputerScience
Minimum Score:130 Maximum Score:715 Average Score:405.6363636363636
--------------------
Subject: MechanicalEngg
Minimum Score:180 Maximum Score:623 Average Score:406.87659574468086
--------------------
Subject: ElectricalEngg
Minimum Score:206 Maximum Score:676 Average Score:433.0372670807453
--------------------
Subject: TelecomEngg
Minimum Score:153 Maximum Score:548 Average Score:350.17379679144386
--------------------
Subject: CivilEngg
Minimum Score:166 Maximum Score:516 Average Score:349.6666666666667
--------------------

Bivariate Analysis

How Many Years an Employee worked in a company.

experince=(df['DOL']-df['DOJ']).dt.components.iloc[:,0]/365
print("Maximum Years Worked in Company:", experince.max())
print("Minimum Years Worked in Company:", experince.min())

Maximum Years Worked in Company: 24.602739726027398
Minimum Years Worked in Company: -0.8301369863013699

There is Something wrong In eaither DOJ or DOL column, Lets fing out

x=df[(df['DOL']-df['DOJ']).dt.components.iloc[:,0]<0]
x.shape[0]

40

38 rows have DOJ greater than the DOL.

DOL Must be greater than the DOJ. But here inverse happened. Lets Impute with the "Present Date" which we were created earlier("01-01-2016").

df.iloc[x.index,3]=dt.datetime(2016,1,1)

df['YearsOfExperience']=(df['DOL']-df['DOJ']).dt.components.iloc[:,0]/365
print("Maximum Years Worked in Company:", df['YearsOfExperience'].max())
print("Minimum Years Worked in Company:", df['YearsOfExperience'].min())
print("Average Years Worked in Company:", df['YearsOfExperience'].mean())

Maximum Years Worked in Company: 24.602739726027398
Minimum Years Worked in Company: 0.0
Average Years Worked in Company: 1.7463505725465414

plt.figure(figsize=(12,4))
sns.histplot(df['YearsOfExperience'], kde=True, color='orange')
plt.xticks(fontsize=12)
plt.title("Distribution of Years an Employee worked in a company", fontsize=18, color='blue');

plt.figure(figsize=(12,4))
sns.boxplot(df['YearsOfExperience'], color='pink')
plt.xticks(fontsize=12)
plt.title("Distribution of Years an Employee worked in a company", fontsize=18, color='blue');

Number of employees with Work experience more than 5 Years

df[df['YearsOfExperience']>5].shape[0]

59

1. A single Employee worked Maximum of 24 Years in a company.
2. Many Employees worked0 Years in a company.
3. Average Work Experience in a company is 1 year and 8 Months.
4. Almost 95% of employees has worked between 0-5 Years in a company.
5. Only 59 Employees has 5+ Years Experience.

Relationship between Years of Experience and Salary

plt.figure(figsize=(12,4))
sns.scatterplot(x=df['YearsOfExperience'], y=df['Salary'], color='orange')
plt.xticks(fontsize=12)
plt.title("Relationship between Years of Experience and Salary", fontsize=18, color='blue');

Lets Drop some Extrame Salaries and Years of Experience and then CHeck the relationship.

rel_sal_exp=df.copy()

Removing Salaries above 2.5 Lakks as there are only 6 Datapoints and Years of Experience above 7.

new_sal=rel_sal_exp[rel_sal_exp['Salary']<2000000]
new_sal_exp=new_sal[new_sal['YearsOfExperience']<=7]

plt.figure(figsize=(12,4))
sns.regplot(x=new_sal_exp['YearsOfExperience'], y=new_sal_exp['Salary'],marker='*', 
            color='orange', line_kws=dict(color='black'))
plt.xticks(fontsize=12)
plt.title("Relationship between Years of Experience and Salary", fontsize=18, color='blue');

There is a Positive Relationship Between Years of Experience & Salary, As Experience increases Salary increases.

Which are the top 15 high paying designation?

plt.figure(figsize=(12,4))
sns.barplot(x=df['Designation'], y=df['Salary'], ci=None,
           order=df.groupby('Designation').Salary.mean().sort_values(ascending=False).index[:15])
plt.xticks(fontsize=12, rotation=70)
plt.title("Top 15 high Paying Job Designation", fontsize=18, color='blue');

Junior Manager, Senior Developer and Data Scientistthese are the Job Designation with high average Salary.

Which Job Designations have Lower PayScale?

plt.figure(figsize=(12,4))
sns.barplot(x=df['Designation'], y=df['Salary'], ci=None,
           order=df.groupby('Designation').Salary.mean().sort_values(ascending=True).index[:15])
plt.xticks(fontsize=12, rotation=70)
plt.title("Low Paying Job Designation", fontsize=18, color='blue');

Secreary, Trainee Software Developer, Visiting Faculty are have low average salary.

Gender wise Salary estimation

plt.figure(figsize=(12,4))
sns.barplot(x=df['Gender'], y=df['Salary'], ci=None, palette='pink_r')
plt.xticks(fontsize=15)
plt.title("Average Salary by Gender", fontsize=18, color='blue');

Average Salary for Male and Female Employee is almost Equal.

In Which Job Designation Female employment is higher?

designation_by_gender=pd.crosstab(index=df['Designation'], columns=df['Gender']).reset_index()
designation_by_gender.columns=['Designation','Female','Male']
designation_by_gender.head()

	Designation	Female	Male
0	.Net Developer	9	26
1	.Net Web Developer	1	3
2	Account Executive	2	2
3	Account Manager	0	1
4	Admin Assistant	0	2

plt.figure(figsize=(12,4))
sns.barplot(x='Designation', y='Female', data=designation_by_gender, ci=None,
           order=designation_by_gender.groupby('Designation').Female.mean().sort_values(ascending=False).index[:15])
plt.xticks(fontsize=12, rotation=70)
plt.title("Top 15 Job Designation for Female", fontsize=18, color='blue');

Most Females working as Software Engineer, System Engineer, Software Developer, Programmer Analyst.

In Which Job Designation Male employment is higher?

plt.figure(figsize=(12,4))
sns.barplot(x='Designation', y='Male', data=designation_by_gender, ci=None,
           order=designation_by_gender.groupby('Designation').Male.mean().sort_values(ascending=False).index[:15])
plt.xticks(fontsize=12, rotation=70)
plt.title("Top 15 Job Designation for Male", fontsize=18, color='blue');

1. Most Males working as Software Engineer, System Engineer, Software Developer, Programmer Analyst.
2. Both Male Female employees preffer Software field.

Is there any Relation between Graduation GPA and Salary?

Here we need to drop some of high salaries so we can see the clear relation

new_sal=df.copy()
new_sal=new_sal[new_sal['Salary']<1500000]

plt.figure(figsize=(12,4))
sns.regplot(y='Salary', x='collegeGPA', data=new_sal, marker='*', 
            color='orange', line_kws=dict(color='black'))
plt.xticks(fontsize=12)
plt.title("Relationship between College GPA and Salary", fontsize=18, color='blue');

There is a Positive relation between College GPA and Salary, but the relation is not stronger.

Relationship between Employebility Text Exam Score and Salary

df['Total_Score']=df['English']+df['Logical']+df['Quant']+df['Domain']

new_sal=df.copy()
new_sal=new_sal[new_sal['Salary']<1500000]

plt.figure(figsize=(12,4))
sns.regplot(y='Salary', x='Total_Score', data=df, marker='*', 
            color='orange', line_kws=dict(color='black'))
plt.xticks(fontsize=12)
plt.title("Relationship between Employability Score and Salary", fontsize=18, color='blue');

Higher is the Test Score higher the chances of getting high paying jobs.

Gender wise Education performanc.

edu_perf=pd.pivot_table(data=df, index='Gender', values=['10percentage','12percentage','collegeGPA'], aggfunc='mean').T
edu_perf.reset_index(inplace=True)
edu_perf.columns=['Percentage','Female','Male']
edu_perf

	Percentage	Female	Male
0	10percentage	80.932894	76.979000
1	12percentage	77.007618	73.666636
2	collegeGPA	74.135517	70.927928

fig=px.bar(data_frame=edu_perf, y='Percentage',x=['Female','Male'], text_auto=True,
          title='Education Performance Comparison by Gender')
fig.show()

In 10th, 12th and even in Graduation the performance of Female student is better than Male😮😮.

Which Graduation Specialization has Highest average GPA?

plt.figure(figsize=(12,4))
sns.barplot(x='Specialization', y='collegeGPA', data=df, ci=None,
           order=df.groupby('Specialization').collegeGPA.mean().sort_values(ascending=False).index[:15])
plt.xticks(fontsize=12, rotation=70)
plt.title("Top 15 Specialization by College GPA", fontsize=18, color='blue');

Embeded Systems Technology has highest average COllege GPA.

Average Salary by Specialization

plt.figure(figsize=(12,4))
sns.barplot(x='Specialization', y='Salary', data=df, ci=None,
           order=df.groupby('Specialization').Salary.mean().sort_values(ascending=False).index[:15])
plt.xticks(fontsize=12, rotation=70)
plt.title("Top 15 Specialization by Average Salary", fontsize=18, color='blue');

df[df['Specialization']=='Polymer Technology']

	ID	Salary	DOJ	DOL	Designation	JobCity	Gender	DOB	10percentage	10board	12graduation	12percentage	12board	CollegeID	CollegeTier	Degree	Specialization	collegeGPA	CollegeCityID	CollegeCityTier	CollegeState	GraduationYear	English	Logical	Quant	Domain	ComputerProgramming	ElectronicsAndSemicon	ComputerScience	MechanicalEngg	ElectricalEngg	TelecomEngg	CivilEngg	conscientiousness	agreeableness	extraversion	nueroticism	openess_to_experience	Year_of_Joining	Year_of_Leaving	Age	YearsOfExperience	Total_Score
990	962376	700000	2014-07-01	2016-01-01	Product Manager	Jaipur	m	1992-01-03	87.4	CBSE	2009	74.0	State Board	436	1	M.Tech./M.E.	Polymer Technology	72.79	436	0	Uttarakhand	2014	625	450	655	-1.0	-1	-1	-1	-1	-1	-1	-1	-0.3027	-0.4536	-0.6048	-0.8682	0.0973	2014	2016	24	1.50411	1729.0

1. Polymer Technology has Highest salary. Becuase there is only Entry for this Specialization and the salary fot that employee is 7lakh.
2. Industrical & Production Engineering, Civil ENgineering, Chemical enginnering specialization has highest average salary.
3. Difference in the Average salary between Polymer Technology specialization and other specialization is huge.

Which State Students have Highest College GPA

plt.figure(figsize=(12,4))
sns.barplot(x='CollegeState', y='collegeGPA', data=df, ci=None)
plt.xticks(fontsize=12, rotation=70)
plt.title("College GPA by State", fontsize=18, color='blue');

1. Jharkhand, West Bengal, Bihar, Tamilnadu and Orissa state student has highest College/Graduation GPA.
2. Maharashtra, Meghalaya, Goa, Sikkim and Rajsthan state student has lowest College/Graduation GPA.

Research Questions

1. Times of India article dated Jan 18, 2019 states that “After doing your Computer Science Engineering if you take up jobs as a Programming Analyst, Software Engineer, Hardware Engineer and Associate Engineer you can earn up to 2.5-3 lakhs as a fresh graduate.” Test this claim with the data given to you.
2. Is there a relationship between gender and specialisation? (i.e. Does the preference of Specialisation depend on the Gender?)

Que-1: Here the Claim is: “After doing your Computer Science Engineering if you take up jobs as a Programming Analyst, Software Engineer, Hardware Engineer and Associate Engineer you can earn up to 2.5-3 lakhs as a fresh graduate.”`

Step-1:

Create DataFrame of Employee who Graduated in Computer Science Engineering descipline/Specialization.

CS=df[df['Specialization']=='Computer Science Engineering']
CS.head(2)

	ID	Salary	DOJ	DOL	Designation	JobCity	Gender	DOB	10percentage	10board	12graduation	12percentage	12board	CollegeID	CollegeTier	Degree	Specialization	collegeGPA	CollegeCityID	CollegeCityTier	CollegeState	GraduationYear	English	Logical	Quant	Domain	ComputerProgramming	ElectronicsAndSemicon	ComputerScience	MechanicalEngg	ElectricalEngg	TelecomEngg	CivilEngg	conscientiousness	agreeableness	extraversion	nueroticism	openess_to_experience	Year_of_Joining	Year_of_Leaving	Age	YearsOfExperience	Total_Score
0	203097	420000	2012-06-01	2016-01-01	Senior Quality Engineer	Bangalore	f	1990-02-19	84.3	State Board	2007	95.8	State Board	1141	2	B.Tech/B.E.	Computer Science Engineering	78.00	1141	0	Andhra Pradesh	2011	515	585	525	0.635979	445	-1	-1	-1	-1	-1	-1	0.9737	0.8128	0.5269	1.3549	-0.4455	2012	2016	25	3.586301	1625.635979
3	267447	1100000	2011-07-01	2016-01-01	Senior Software Engineer	Gurgaon	m	1989-12-05	85.6	CBSE	2007	83.6	CBSE	6920	1	B.Tech/B.E.	Computer Science Engineering	74.64	6920	1	Delhi	2011	635	585	625	0.974396	615	-1	-1	-1	-1	-1	-1	0.0464	0.3448	-0.3440	-0.4078	-0.9194	2011	2016	26	4.506849	1845.974396

CS.shape[0]

1354

There 1354 Employees Studies in Computer Science Engineering

Step-2

Create DataFrame of Computer Science Engineering graduates working in Programming Analyst, Software Engineer, Hardware Engineer and Associate Engineer these job Roles/Designations.

req_des=CS[(CS['Designation']=='Program Analyst Trainee') | 
           (CS['Designation']=='Programmer Analyst Trainee') |
           (CS['Designation']=='Software Engineer') | 
           (CS['Designation']=='Hardware Engineer and Associate Engineer') |
           (CS['Designation']=='Associate Software Engineer') |
           (CS['Designation']=='Software Programmer') |
           (CS['Designation']=='Software Engineer Trainee') |
           (CS['Designation']=='Software Trainee') |
           (CS['Designation']=='Associate Software Engineer')]
req_des.head(2)

	ID	Salary	DOJ	DOL	Designation	JobCity	Gender	DOB	10percentage	10board	12graduation	12percentage	12board	CollegeID	CollegeTier	Degree	Specialization	collegeGPA	CollegeCityID	CollegeCityTier	CollegeState	GraduationYear	English	Logical	Quant	Domain	ComputerProgramming	ElectronicsAndSemicon	ComputerScience	MechanicalEngg	ElectricalEngg	TelecomEngg	CivilEngg	conscientiousness	agreeableness	extraversion	nueroticism	openess_to_experience	Year_of_Joining	Year_of_Leaving	Age	YearsOfExperience	Total_Score
19	466888	325000	2014-09-01	2016-01-01	Software Engineer	Pune	f	1990-11-30	79.0	CBSE	2008	62.20	CBSE	7977	2	B.Tech/B.E.	Computer Science Engineering	76.30	7977	0	Uttar Pradesh	2012	485	445	435	0.864685	525	-1	-1	-1	-1	-1	-1	0.8192	0.2668	-0.2714	-0.4078	-0.1295	2014	2016	25	1.334247	1365.864685
20	140069	320000	2010-11-01	2012-09-01	Software Engineer	Bangalore	f	1988-07-25	91.2	State Board	2006	84.63	State Board	4437	2	B.Tech/B.E.	Computer Science Engineering	78.15	4437	0	Karnataka	2010	455	535	445	0.635979	445	-1	-1	-1	-1	-1	-1	-0.1082	0.9688	0.5269	-0.2902	0.3444	2010	2012	27	1.835616	1435.635979

req_des.shape[0]

282

283 Employees from Computer Science Background working as related to Programming Analyst or Software Engineer or Hardware Engineer and Associate Engineer.

Step-3

Check whether the Average Salary for these Job Designation Lies Between 2.5 to 3 Lakh or Not.

plt.figure(figsize=(12,4))
sns.histplot(x=req_des['Salary'], kde=True, color='orange')
plt.xticks(fontsize=12)
plt.title("Salary Distribution for Perticular Designation", fontsize=18, color='blue');

plt.figure(figsize=(12,4))
sns.boxplot(x=req_des['Salary'], color='orange')
plt.xticks(fontsize=12)
plt.title("Salary Distribution for Perticular Designation", fontsize=18, color='blue');

There are some extreame data points in salary its better to take median

req_des['Salary'].median()

325000.0

Clearly See that from the Average Salary that "Computer Science Engineering" Students Who is working either in "Programming Analyst or Software Engineer or Hardware Engineer and Associate Engineer" Designation get starting average Salary above "3.2 Lakhs INR". Hence the "Claim of Times Of India is Correct".

Que-2 : Is there a relationship between gender and specialisation? (i.e. Does the preference of Specialisation depend on the Gender?)

gender_by_specialization=pd.crosstab(index=df['Gender'], columns=df['Specialization']).T
gender_by_specialization.reset_index(inplace=True)
gender_by_specialization.columns=['Specialization', 'Female','Male']
gender_by_specialization

	Specialization	Female	Male
0	Aeronautical Engineering	1	2
1	Automobile/Automotive Engineering	0	5
2	Biomedical Engineering	2	0
3	Biotechnology	9	6
4	Ceramic Engineering	0	1
5	Chemical Engineering	1	8
6	Civil Engineering	6	23
7	Computer Application	59	185
8	Computer Science Engineering	361	993
9	Electrical Engineering	51	229
10	Electronics & Communication Engineering	215	688
11	Electronics & Instrumentation Engineering	26	67
12	Electronics & Telecommunication Engineering	29	98
13	Embedded Systems Technology	0	1
14	Industrial & Production Engineering	2	8
15	Industrial Engineering	1	2
16	Information Technology/Science	183	507
17	Mechanical Engineering	11	201
18	Metallurgical Engineering	0	2
19	Other	0	13
20	Polymer Technology	0	1
21	Power Systems And Automation	0	1

Most common Specialization in Female & Male

fig=px.pie(names='Specialization', values='Female', data_frame=gender_by_specialization,
          title='Most common Specialization in Female')
fig.show()

fig=px.pie(names='Specialization', values='Male', data_frame=gender_by_specialization,
          title='Most common Specialization in Male')
fig.show()

Top 5 Most Common Specialization for Female are "Computer Science, Electronics & Communinication, Information Technology, Computer Application and Electrical Engineering.

Top 5 Most Common Specialization for Male are "Computer Science, Electronics & Communinication, Information Technology, Electrical Engineering & Mechanical Engineering.

Both Male and Female have same Interest of studies so we cannot say that Specialization Depends on Gender.